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Deception and cloud integration: A multi-layered approach for DDoS detection, mitigation, and attack surface minimization in SD-IoT networks

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computers and Electrical engineering 2025年 126卷

作者： Ameer El-Sayed Ahmed A. Toony Amr Tolba Fayez Alqahtani Yasser Alginahi Wael Said Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig 44511 Egypt Department of Business Information Systems High Institute for Commercial Sciences (Aboukir High Institutes) Ministry of Higher Education and Scientific Research Alexandria Egypt Computer Science Department Community College King Saud University Riyadh 11437 Saudi Arabia Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Department of Computer Science Adrian College Michigan USA Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig 44511 Egypt

Detecting Distributed Denial of Service (DDoS) attacks in Software-Defined Internet of Things (SD-IoT) networks is challenging due to vulnerabilities in single-controller architectures, the limitations of the OpenFlow protocol, evolving DDoS strategies, and resource constraints. This research proposes a multi-layered security framework that integrates deception-based security, cloud-integrated machine learning (ML), a new hierarchically distributed multi-controller (HDMC) architecture, P4-enabled real-time traffic monitoring, and adaptive mitigation. The framework includes dynamic time-based windowing for enhanced detection, a decoy network to divert attackers, and a cloud-based multi-task ML model (MT-EDD) for attack classification. It also features a synchronized multi-control design for secure communication and coordinated actions among multiple controllers and a dynamic monitoring algorithm for real-time traffic analysis. P4 switches extract features from network traffic and send them to a cloud-based server for preprocessing and analysis by a pre-trained ensemble learning model (MT-EDD), which predicts attack states and communicates results to the central controller for mitigation. The controller then enforces appropriate mitigation actions on P4 switches. This approach offloads computationally intensive tasks to the cloud, improving scalability and detection accuracy. Evaluations show the framework achieves an average accuracy of 98.42%, precision of 96.17%, recall of 94.72%, F1-score of 95.39%, and specificity of 98.22%. The proposed P4-enabled solution consumes 30% less bandwidth and 25% less CPU, reduces detection times by 54.3%, and improves detection accuracy by 5.2% compared to the OpenFlow-enabled method. The HDMC architecture, evaluated against a single-controller setup, demonstrated 40% higher throughput and 32% lower latency, confirming its superior performance across multiple metrics.

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Resilient intrusion detection system for cloud containers

Resilient intrusion detection system for cloud containers

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作者： Abed, Amr S. Azab, Mohamed Clancy, Charles Kashkoush, Mona S. Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Informatics Research Institute City of Scientific Research and Technological Applications Alexandria Egypt Hume Center for National Security and Technology Virginia Tech ArlingtonVA United States

The lightweight virtualisation and isolated execution offered by Linux containers qualify it to be the dominant virtualisation platform for cloud-based applications. The fact that Linux containers run on the same host while sharing the same kernel opens the door for new attacks. However, limited research has been conducted in the area of securing cloud containers. This paper presents a resilient intrusion detection and resolution system for cloud-based containers. The system relies on two main pillars, a real-time smart behaviour monitoring mechanism to detect maliciously behaving containers, and a moving-target defence approach that applies runtime container migration to quarantine such containers and to minimise attack dispersion. To avoid zero-day targeted attacks, the system also induces random live migrations between running containers to obfuscate its execution behaviour. Such obfuscation makes it harder for attackers to execute their targeted attacks. The system was tested by a big-data application using a container-based Apache Hadoop cluster to demonstrate the system's ability to automatically deploy, monitor, detect, and respond to maliciously behaving applications by live migration or by rolling back the container to a safe state. Results showed that the proposed system efficiently ensure safe and secure container operation. © 2020 Inderscience Enterprises Ltd.

关键词： Containers

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Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer

arXiv

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arXiv 2024年

作者： Dong, Zehao Zhao, Qihang Payne, Philip R.O. Province, Michael A. Cruchaga, Carlos Zhang, Muhan Zhao, Tianyu Chen, Yixin Li, Fuhai Department of Computer Science & Engineering Washington University St. Louis United States Washington University School of Medicine St. Louis United States Department of Pediatrics Washington University School of Medicine St. Louis United States Institute for Artificial Intelligence Peking University China Department of Genetics Washington University School of Medicine St. Louis United States Department of Psychiatry Washington University School of Medicine St. Louis United States Department of Radiation Oncology Washington University School of Medicine St. Louis United States Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong

Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data analysis, i.e., limited-prediction/diagnosis accuracy and limited-reproducible biomarker identification capacity across multiple datasets. The root of the challenges is the unique graph structure of biological signaling pathways, which consists of a large number of targets and intensive and complex signaling interactions among these targets. To resolve these two challenges, in this study, we presented a novel GNN model architecture, named PathFormer, which systematically integrate signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis. In the comparison results, PathFormer outperformed existing GNN models significantly in terms of highly accurate prediction capability ( 30% accuracy improvement in disease diagnosis compared with existing GNN models) and high reproducibility of biomarker ranking across different datasets. The improvement was confirmed using two independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The PathFormer model can be directly applied to other omics data analysis studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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The Dynamics and Control of the Fractional Forms of Some Rational Chaotic Maps

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Journal of Systems Science & Complexity 2020年第3期33卷 584-603页

作者： OUANNAS Adel KHENNAOUI Amina-Aicha BENDOUKHA Samir WANG Zhen PHAM Viet-Thanh Laboratory of Mathematics Informatics and Systems(LAMIS)University of Larbi TebessiTebessa 12002Algeria Department of Mathematics and Computer Sciences University of Larbi Ben M'hidiOum El BouaghiAlgeria Department of Electrical Engineering College of EngineeringYanbuTaibah UniversitySaudi Arabia Shaanxi Engineering Research Center of Controllable Neutron Source School of ScienceXijing UniversityXi'an 710123China Faculty of Electrical and Electronic Engineering Phenikaa Institute for Advanced Study(PIAS)Phenikaa UniversityYen NghiaHa Dong districtHanoi 100000Vietnam Phenikaa Research and Technology Institute(PRATI) A&A Green Phoenix Group167 Hoang NganHanoi 100000Vietnam

This paper proposes three fractional discrete chaotic systems based on the Rulkov,Chang,and Zeraoulia–Sprott rational *** dynamics of the proposed maps are investigated by means of phase plots and bifurcations *** stabilization schemes are proposed for each of the three maps and the convergence of the states is established by using the Lyapunov ***,a combination synchronization scheme is proposed whereby a combination of the fractional Rulkov and Chang maps is synchronized to the fractional Zeraoulia-Sprott *** results are used to confirm the findings of the paper.

关键词： Fractional discrete chaos fractional rational maps stabilization synchronization

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Fusion of Super-Resolution and Semantic Segmentation Deep Models for Building Footprint Extraction From Aerial Images

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The Photogrammetric Record 2025年第190期40卷

作者： Ziyi Chen Yuhua Luo Jiaying Zhang Ronggang Guo Liai Deng Jinghua Liu Dilong Li Yongtao Yu Ammar Abulibdeh Cheng Wang Department of Computer Science and Technology Fujian Key Laboratory of big Data Intelligence and Security Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen FJ China Guojiao Spatial Information Technology (Beijing) co. Ltd Beijing China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian JS China Department of Humanities College of Arts and Sciences Qatar University Doha Qatar School of Informatics Xiamen University Xiamen FJ China

The spatial resolution of remotely sensed images has seen significant improvements; higher resolution facilitates the understanding of remote sensing images and improves the accuracy of building footprint extraction. Thus, this paper introduces a novel approach that combines super-resolution reconstruction with semantic segmentation deep models for building footprint extraction. To begin, a super-resolution reconstruction deep model is employed to enhance the resolution of the target images. Subsequently, training samples are generated using various data augmentation techniques based on the reconstructed images. Finally, a semantic segmentation deep model is trained to perform the final building footprint extraction. For validation, our proposed framework was tested on two publicly available aerial image datasets: the WHU building dataset and the Massachusetts building dataset. The results obtained from these datasets demonstrated the effectiveness of our method, achieving high Intersection over Union (IoU) scores of 90.57% and 75.02%, respectively. Furthermore, our approach outperformed seven other competing methods, showcasing its superior performance.

关键词： aerial image building footprint extraction deep learning semantic segmentation super-resolution

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BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery

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computers in Biology and Medicine 2023年 167卷 107602-107602页

作者： Plotka, Szymon Grzeszczyk, Michal K. Brawura-Biskupski-Samaha, Robert Gutaj, Pawel Lipa, Michal Trzciński, Tomasz Išgum, Ivana Sánchez, Clara I. Sitek, Arkadiusz Sano Centre for Computational Medicine Cracow Poland Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center Amsterdam Netherlands Second Department of Obstetrics and Gynecology The Medical Centre of Postgraduate Education Warsaw Poland Department of Reproduction Poznan University of Medical Sciences Poznan Poznan Poland First Department of Obstetrics and Gynecology Medical University of Warsaw Warsaw Poland Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Radiology and Nuclear Medicine Amsterdam University Medical Center location University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise. © 2023 The Author(s)

关键词： Forecasting

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Cyber-Physical System for Industrial Automation Using Quantum Deep Learning

Cyber-Physical System for Industrial Automation Using Quantu...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Anand Singh Rajawat S.B. Goyal Pradeep Bedi Neagu Bogdan Constantin Maria Simona Raboaca Chaman Verma School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Galgotais University Greater Noida India Department of Power Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Informatics University of Eotvos Lorand Budapest Hungary

ISBN: (纸本)9781665487351

Automation industries are using Industry 4.0 solutions for several reasons. Still, the most important is to make it easier for their employees to get secure information and to work better with people in other departments. The goal of this effort is to improve productivity and efficiency by making it easier to make good decisions at the right time. This will make it possible to make these kinds of improvements. Constraint-based security solutions are becoming increasingly popular as the digital and physical worlds become more connected. Based on the processing speed, the amount of memory that can be used, and the amount of backup power that can be used. The algorithms and security frameworks used in earlier research need many resources. Several researchers have given an overview of the many security problems with the CPS and their studies and recommendations. But constraint-based applications need light security systems that can work with hardware with limited capabilities. We proposed a safety mechanism that can be used with the already-made constraint-based CPS program. To propose Cyber-Physical System for Industrial Automation Using Quantum Deep Learning, We proposed Cyber-Physical System for Industrial Automation that uses Quantum Learning to find attacking patterns.

关键词： Deep learning Productivity Memory management Cyber-physical systems Market research Fourth Industrial Revolution Safety

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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C3BAM-XAI: Convolutional Block Attention Module Enhanced Explainable Artificial Intelligence-Based Parkinson’s Disease Stage Classification

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Cognitive Computation 2025年第3期17卷

作者： Abbas, Muhammad John Khan, Muhammad Attique Hamza, Ameer Alsenan, Shrooq Rehman, Aleesha Baili, Jamel Zhang, Yudong Department of Computer Science HITEC University Taxila47080 Pakistan Center of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas51368 Lithuania Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Mang Pakistan Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Informatics University of Leicester Leicester United Kingdom

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes significant impairment in neurons, physiological structures, and behavior of people. However, these changes are very subtle in the early stages of PD, making diagnosis and treatment challenging. To overcome such challenges, we proposed a novel convolutional neural network (CNN) architecture called C3BAM-XAI that is based on the convolutional block attention module (CBAM) enhanced CNN and explainable artificial intelligence (XAI). In the proposed architecture, the data imbalance issue was resolved at the initial stage by employing augmentation techniques for the training data. After that, the proposed CNN architecture is designed with CBAM. The CBAM architecture is based on two sub-modules—the channel attention module and the spatial attention module. The input of the initial convolutional layer is passed to the channel attention module, which is further passed to the spatial attention module for more critical information extraction. For smooth training, hyperparameters are selected using Nadam Optimization (NO) instead of manual assignment. In the testing phase, features are extracted from the dense layer and passed to several neural network classifiers. In addition to this, the designed model is interpreted using the explainable AI technique to see the predicted label and insight decision features. The experimental process was performed on the publically available PD Kaggle dataset and obtained an improved accuracy of 93.33%. In addition, several ablation studies were conducted, and it is concluded that the CBAM not only improved accuracy but also made this model a reliable tool for clinical diagnosis, both in terms of accuracy and interpretability. The results thus open new avenues toward applying deep learning models practically for applications in the medical domain and early-stage detection of Parkinson’s disease, thereby helping to improve patient outcomes. © The Author(s), under

关键词： Convolutional neural networks

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

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arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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